Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations5410
Missing cells370
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory387.3 B

Variable types

Text1
Numeric8
Categorical3

Alerts

Fraud_Probability is highly overall correlated with Fraud_Probability_Percent and 1 other fieldsHigh correlation
Fraud_Probability_Percent is highly overall correlated with Fraud_Probability and 1 other fieldsHigh correlation
Top1_SHAP is highly overall correlated with Fraud_Probability and 1 other fieldsHigh correlation
Top3_ActualValue is highly overall correlated with Top3_FeatureHigh correlation
Top3_Feature is highly overall correlated with Top3_ActualValueHigh correlation
Top1_Feature is highly imbalanced (74.2%) Imbalance
Top3_ActualValue has 317 (5.9%) missing values Missing
Provider has unique values Unique
Top2_ActualValue has 515 (9.5%) zeros Zeros
Top3_ActualValue has 693 (12.8%) zeros Zeros

Reproduction

Analysis started2025-06-10 13:52:43.011235
Analysis finished2025-06-10 13:52:56.552816
Duration13.54 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Provider
Text

Unique 

Distinct5410
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size343.5 KiB
2025-06-10T13:52:56.960161image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters43280
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5410 ?
Unique (%)100.0%

Sample

1st rowPRV52145
2nd rowPRV55104
3rd rowPRV54894
4th rowPRV54927
5th rowPRV55215
ValueCountFrequency (%)
prv56021 1
 
< 0.1%
prv55405 1
 
< 0.1%
prv51787 1
 
< 0.1%
prv51875 1
 
< 0.1%
prv55810 1
 
< 0.1%
prv56873 1
 
< 0.1%
prv56381 1
 
< 0.1%
prv51213 1
 
< 0.1%
prv56582 1
 
< 0.1%
prv52781 1
 
< 0.1%
Other values (5400) 5400
99.8%
2025-06-10T13:52:57.636007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 7865
18.2%
P 5410
12.5%
R 5410
12.5%
V 5410
12.5%
1 2495
 
5.8%
6 2452
 
5.7%
3 2448
 
5.7%
2 2438
 
5.6%
4 2433
 
5.6%
7 2248
 
5.2%
Other values (3) 4671
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 7865
18.2%
P 5410
12.5%
R 5410
12.5%
V 5410
12.5%
1 2495
 
5.8%
6 2452
 
5.7%
3 2448
 
5.7%
2 2438
 
5.6%
4 2433
 
5.6%
7 2248
 
5.2%
Other values (3) 4671
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 7865
18.2%
P 5410
12.5%
R 5410
12.5%
V 5410
12.5%
1 2495
 
5.8%
6 2452
 
5.7%
3 2448
 
5.7%
2 2438
 
5.6%
4 2433
 
5.6%
7 2248
 
5.2%
Other values (3) 4671
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 7865
18.2%
P 5410
12.5%
R 5410
12.5%
V 5410
12.5%
1 2495
 
5.8%
6 2452
 
5.7%
3 2448
 
5.7%
2 2438
 
5.6%
4 2433
 
5.6%
7 2248
 
5.2%
Other values (3) 4671
10.8%

Fraud_Probability
Real number (ℝ)

High correlation 

Distinct5401
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15013172
Minimum0.00022823537
Maximum0.99438226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2025-06-10T13:52:57.861005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.00022823537
5-th percentile0.00098103074
Q10.0031666447
median0.010029438
Q30.070688568
95-th percentile0.93668438
Maximum0.99438226
Range0.99415402
Interquartile range (IQR)0.067521923

Descriptive statistics

Standard deviation0.29431393
Coefficient of variation (CV)1.9603714
Kurtosis2.3045603
Mean0.15013172
Median Absolute Deviation (MAD)0.0086339168
Skewness1.9765938
Sum812.2126
Variance0.086620688
MonotonicityNot monotonic
2025-06-10T13:52:58.133444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001222939 3
 
0.1%
0.0009220703 3
 
0.1%
0.0015959388 2
 
< 0.1%
0.000902706 2
 
< 0.1%
0.0004993615 2
 
< 0.1%
0.002283027 2
 
< 0.1%
0.001508889 2
 
< 0.1%
0.043603275 1
 
< 0.1%
0.0013410501 1
 
< 0.1%
0.0012702412 1
 
< 0.1%
Other values (5391) 5391
99.6%
ValueCountFrequency (%)
0.00022823537 1
< 0.1%
0.0002561417 1
< 0.1%
0.00026063184 1
< 0.1%
0.000264258 1
< 0.1%
0.00027018946 1
< 0.1%
0.00027636546 1
< 0.1%
0.00028678545 1
< 0.1%
0.00029498356 1
< 0.1%
0.00031468808 1
< 0.1%
0.00032562297 1
< 0.1%
ValueCountFrequency (%)
0.99438226 1
< 0.1%
0.99432385 1
< 0.1%
0.9941806 1
< 0.1%
0.9940916 1
< 0.1%
0.9940706 1
< 0.1%
0.9939267 1
< 0.1%
0.9933137 1
< 0.1%
0.99322236 1
< 0.1%
0.99285537 1
< 0.1%
0.9923529 1
< 0.1%

Top1_Feature
Categorical

Imbalance 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size505.1 KiB
Provider_Insurance_Claim_Reimbursement_Amt
4497 
HospitalDuration_max
479 
prv_avg_claims_indicator
 
339
DeductibleAmtPaid_sum
 
45
ChronicCond_Heartfailure_mean
 
16
Other values (7)
 
34

Length

Max length42
Median length42
Mean length38.573567
Min length17

Characters and Unicode

Total characters208683
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowProvider_Insurance_Claim_Reimbursement_Amt
2nd rowProvider_Insurance_Claim_Reimbursement_Amt
3rd rowProvider_Insurance_Claim_Reimbursement_Amt
4th rowProvider_Insurance_Claim_Reimbursement_Amt
5th rowProvider_Insurance_Claim_Reimbursement_Amt

Common Values

ValueCountFrequency (%)
Provider_Insurance_Claim_Reimbursement_Amt 4497
83.1%
HospitalDuration_max 479
 
8.9%
prv_avg_claims_indicator 339
 
6.3%
DeductibleAmtPaid_sum 45
 
0.8%
ChronicCond_Heartfailure_mean 16
 
0.3%
perc_allocated_used 14
 
0.3%
ClaimDuration_max 7
 
0.1%
HospitalDuration_std 7
 
0.1%
ChronicCond_Cancer_mean 3
 
0.1%
HospitalDuration_mean 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2025-06-10T13:52:58.382529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
provider_insurance_claim_reimbursement_amt 4497
83.1%
hospitalduration_max 479
 
8.9%
prv_avg_claims_indicator 339
 
6.3%
deductibleamtpaid_sum 45
 
0.8%
chroniccond_heartfailure_mean 16
 
0.3%
perc_allocated_used 14
 
0.3%
claimduration_max 7
 
0.1%
hospitalduration_std 7
 
0.1%
chroniccond_cancer_mean 3
 
0.1%
hospitalduration_mean 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 22680
10.9%
_ 19615
 
9.4%
r 19233
 
9.2%
m 18934
 
9.1%
i 15627
 
7.5%
n 14390
 
6.9%
a 11616
 
5.6%
t 10442
 
5.0%
s 9890
 
4.7%
u 9609
 
4.6%
Other values (20) 56647
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 208683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 22680
10.9%
_ 19615
 
9.4%
r 19233
 
9.2%
m 18934
 
9.1%
i 15627
 
7.5%
n 14390
 
6.9%
a 11616
 
5.6%
t 10442
 
5.0%
s 9890
 
4.7%
u 9609
 
4.6%
Other values (20) 56647
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 208683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 22680
10.9%
_ 19615
 
9.4%
r 19233
 
9.2%
m 18934
 
9.1%
i 15627
 
7.5%
n 14390
 
6.9%
a 11616
 
5.6%
t 10442
 
5.0%
s 9890
 
4.7%
u 9609
 
4.6%
Other values (20) 56647
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 208683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 22680
10.9%
_ 19615
 
9.4%
r 19233
 
9.2%
m 18934
 
9.1%
i 15627
 
7.5%
n 14390
 
6.9%
a 11616
 
5.6%
t 10442
 
5.0%
s 9890
 
4.7%
u 9609
 
4.6%
Other values (20) 56647
27.1%

Top1_SHAP
Real number (ℝ)

High correlation 

Distinct5401
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2353862
Minimum-2.6842446
Maximum2.6063855
Zeros0
Zeros (%)0.0%
Negative4470
Negative (%)82.6%
Memory size42.4 KiB
2025-06-10T13:52:58.615847image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-2.6842446
5-th percentile-2.4068657
Q1-2.1729992
median-1.7461665
Q3-0.92443412
95-th percentile1.4429291
Maximum2.6063855
Range5.2906301
Interquartile range (IQR)1.2485651

Descriptive statistics

Standard deviation1.2945363
Coefficient of variation (CV)-1.0478798
Kurtosis0.66806759
Mean-1.2353862
Median Absolute Deviation (MAD)0.53342845
Skewness1.3444188
Sum-6683.4394
Variance1.6758243
MonotonicityNot monotonic
2025-06-10T13:52:58.894965image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.0813987 3
 
0.1%
-2.1875658 2
 
< 0.1%
-2.183212 2
 
< 0.1%
-2.2915285 2
 
< 0.1%
-2.1374512 2
 
< 0.1%
-2.3748865 2
 
< 0.1%
-2.0690496 2
 
< 0.1%
-2.1070178 2
 
< 0.1%
-1.8546634 1
 
< 0.1%
-2.093958 1
 
< 0.1%
Other values (5391) 5391
99.6%
ValueCountFrequency (%)
-2.6842446 1
< 0.1%
-2.681831 1
< 0.1%
-2.6625078 1
< 0.1%
-2.6603427 1
< 0.1%
-2.6593232 1
< 0.1%
-2.6525118 1
< 0.1%
-2.65052 1
< 0.1%
-2.6447027 1
< 0.1%
-2.6397154 1
< 0.1%
-2.629413 1
< 0.1%
ValueCountFrequency (%)
2.6063855 1
< 0.1%
2.547455 1
< 0.1%
2.511147 1
< 0.1%
2.4954474 1
< 0.1%
2.4939666 1
< 0.1%
2.4802988 1
< 0.1%
2.4797082 1
< 0.1%
2.471098 1
< 0.1%
2.4659982 1
< 0.1%
2.4599466 1
< 0.1%

Top2_Feature
Categorical

Distinct29
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size425.5 KiB
DeductibleAmtPaid_sum
1720 
prv_avg_claims_indicator
1569 
HospitalDuration_max
1089 
ChronicCond_Heartfailure_mean
286 
Provider_Insurance_Claim_Reimbursement_Amt
268 
Other values (24)
478 

Length

Max length42
Median length39
Mean length23.511091
Min length17

Characters and Unicode

Total characters127195
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowprv_avg_claims_indicator
2nd rowDeductibleAmtPaid_sum
3rd rowHospitalDuration_max
4th rowDeductibleAmtPaid_sum
5th rowHospitalDuration_max

Common Values

ValueCountFrequency (%)
DeductibleAmtPaid_sum 1720
31.8%
prv_avg_claims_indicator 1569
29.0%
HospitalDuration_max 1089
20.1%
ChronicCond_Heartfailure_mean 286
 
5.3%
Provider_Insurance_Claim_Reimbursement_Amt 268
 
5.0%
Avg_InscClaimAmtReimbursed_Per_Provider 96
 
1.8%
HospitalDuration_std 86
 
1.6%
ClaimDuration_max 74
 
1.4%
ChronicCond_KidneyDisease_mean 64
 
1.2%
ChronicCond_Cancer_mean 45
 
0.8%
Other values (19) 113
 
2.1%

Length

2025-06-10T13:52:59.182209image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
deductibleamtpaid_sum 1720
31.8%
prv_avg_claims_indicator 1569
29.0%
hospitalduration_max 1089
20.1%
chroniccond_heartfailure_mean 286
 
5.3%
provider_insurance_claim_reimbursement_amt 268
 
5.0%
avg_inscclaimamtreimbursed_per_provider 96
 
1.8%
hospitalduration_std 86
 
1.6%
claimduration_max 74
 
1.4%
chroniccond_kidneydisease_mean 64
 
1.2%
chroniccond_cancer_mean 45
 
0.8%
Other values (19) 113
 
2.1%

Most occurring characters

ValueCountFrequency (%)
i 12689
 
10.0%
a 12030
 
9.5%
_ 9999
 
7.9%
t 8580
 
6.7%
m 8143
 
6.4%
r 7002
 
5.5%
e 6561
 
5.2%
d 6102
 
4.8%
c 5745
 
4.5%
u 5728
 
4.5%
Other values (23) 44616
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127195
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 12689
 
10.0%
a 12030
 
9.5%
_ 9999
 
7.9%
t 8580
 
6.7%
m 8143
 
6.4%
r 7002
 
5.5%
e 6561
 
5.2%
d 6102
 
4.8%
c 5745
 
4.5%
u 5728
 
4.5%
Other values (23) 44616
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127195
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 12689
 
10.0%
a 12030
 
9.5%
_ 9999
 
7.9%
t 8580
 
6.7%
m 8143
 
6.4%
r 7002
 
5.5%
e 6561
 
5.2%
d 6102
 
4.8%
c 5745
 
4.5%
u 5728
 
4.5%
Other values (23) 44616
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127195
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 12689
 
10.0%
a 12030
 
9.5%
_ 9999
 
7.9%
t 8580
 
6.7%
m 8143
 
6.4%
r 7002
 
5.5%
e 6561
 
5.2%
d 6102
 
4.8%
c 5745
 
4.5%
u 5728
 
4.5%
Other values (23) 44616
35.1%

Top2_SHAP
Real number (ℝ)

Distinct5399
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.48001872
Minimum-1.3274089
Maximum1.228544
Zeros0
Zeros (%)0.0%
Negative4593
Negative (%)84.9%
Memory size42.4 KiB
2025-06-10T13:52:59.450253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-1.3274089
5-th percentile-0.98094258
Q1-0.73744841
median-0.63287482
Q3-0.5258627
95-th percentile0.70683187
Maximum1.228544
Range2.5559529
Interquartile range (IQR)0.21158571

Descriptive statistics

Standard deviation0.50587635
Coefficient of variation (CV)-1.053868
Kurtosis1.4823705
Mean-0.48001872
Median Absolute Deviation (MAD)0.10559684
Skewness1.6475048
Sum-2596.9013
Variance0.25591088
MonotonicityNot monotonic
2025-06-10T13:52:59.683015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6204952 3
 
0.1%
-0.55736446 2
 
< 0.1%
-0.61724293 2
 
< 0.1%
-0.52102983 2
 
< 0.1%
-0.79280275 2
 
< 0.1%
-0.63384634 2
 
< 0.1%
-0.54998827 2
 
< 0.1%
-0.7687515 2
 
< 0.1%
-0.51584965 2
 
< 0.1%
-0.71805626 2
 
< 0.1%
Other values (5389) 5389
99.6%
ValueCountFrequency (%)
-1.3274089 1
< 0.1%
-1.3216667 1
< 0.1%
-1.2959567 1
< 0.1%
-1.271386 1
< 0.1%
-1.2682517 1
< 0.1%
-1.2627395 1
< 0.1%
-1.2499744 1
< 0.1%
-1.2422806 1
< 0.1%
-1.2310311 1
< 0.1%
-1.2197806 1
< 0.1%
ValueCountFrequency (%)
1.228544 1
< 0.1%
1.157284 1
< 0.1%
1.1402673 1
< 0.1%
1.1113061 1
< 0.1%
1.088396 1
< 0.1%
1.0832257 1
< 0.1%
1.0674474 1
< 0.1%
1.0607847 1
< 0.1%
1.0555196 1
< 0.1%
1.0528997 1
< 0.1%

Top3_Feature
Categorical

High correlation 

Distinct36
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size434.3 KiB
DeductibleAmtPaid_sum
862 
prv_avg_claims_indicator
834 
HospitalDuration_max
729 
Avg_InscClaimAmtReimbursed_Per_Provider
545 
ChronicCond_Heartfailure_mean
514 
Other values (31)
1926 

Length

Max length42
Median length36
Mean length25.175231
Min length17

Characters and Unicode

Total characters136198
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDeductibleAmtPaid_sum
2nd rowHospitalDuration_max
3rd rowOperatingPhysician_nunique
4th rowprv_avg_claims_indicator
5th rowHospitalDuration_sum

Common Values

ValueCountFrequency (%)
DeductibleAmtPaid_sum 862
15.9%
prv_avg_claims_indicator 834
15.4%
HospitalDuration_max 729
13.5%
Avg_InscClaimAmtReimbursed_Per_Provider 545
10.1%
ChronicCond_Heartfailure_mean 514
9.5%
ChronicCond_KidneyDisease_mean 342
 
6.3%
HospitalDuration_std 306
 
5.7%
ClaimDuration_max 284
 
5.2%
Provider_Insurance_Claim_Reimbursement_Amt 211
 
3.9%
ChronicCond_Cancer_mean 124
 
2.3%
Other values (26) 659
12.2%

Length

2025-06-10T13:52:59.920880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
deductibleamtpaid_sum 862
15.9%
prv_avg_claims_indicator 834
15.4%
hospitalduration_max 729
13.5%
avg_inscclaimamtreimbursed_per_provider 545
10.1%
chroniccond_heartfailure_mean 514
9.5%
chroniccond_kidneydisease_mean 342
 
6.3%
hospitalduration_std 306
 
5.7%
claimduration_max 284
 
5.2%
provider_insurance_claim_reimbursement_amt 211
 
3.9%
chroniccond_cancer_mean 124
 
2.3%
Other values (26) 659
12.2%

Most occurring characters

ValueCountFrequency (%)
i 12326
 
9.1%
a 11800
 
8.7%
_ 10079
 
7.4%
e 9306
 
6.8%
r 8877
 
6.5%
m 8219
 
6.0%
n 7871
 
5.8%
t 7605
 
5.6%
o 6886
 
5.1%
d 6073
 
4.5%
Other values (24) 47156
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 12326
 
9.1%
a 11800
 
8.7%
_ 10079
 
7.4%
e 9306
 
6.8%
r 8877
 
6.5%
m 8219
 
6.0%
n 7871
 
5.8%
t 7605
 
5.6%
o 6886
 
5.1%
d 6073
 
4.5%
Other values (24) 47156
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 12326
 
9.1%
a 11800
 
8.7%
_ 10079
 
7.4%
e 9306
 
6.8%
r 8877
 
6.5%
m 8219
 
6.0%
n 7871
 
5.8%
t 7605
 
5.6%
o 6886
 
5.1%
d 6073
 
4.5%
Other values (24) 47156
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 12326
 
9.1%
a 11800
 
8.7%
_ 10079
 
7.4%
e 9306
 
6.8%
r 8877
 
6.5%
m 8219
 
6.0%
n 7871
 
5.8%
t 7605
 
5.6%
o 6886
 
5.1%
d 6073
 
4.5%
Other values (24) 47156
34.6%

Top3_SHAP
Real number (ℝ)

Distinct5398
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.37359158
Minimum-1.1483903
Maximum0.9962663
Zeros0
Zeros (%)0.0%
Negative4670
Negative (%)86.3%
Memory size42.4 KiB
2025-06-10T13:53:00.143894image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-1.1483903
5-th percentile-0.75700851
Q1-0.5617617
median-0.47057586
Q3-0.35199756
95-th percentile0.50379438
Maximum0.9962663
Range2.1446566
Interquartile range (IQR)0.20976415

Descriptive statistics

Standard deviation0.36255592
Coefficient of variation (CV)-0.97046063
Kurtosis2.0318449
Mean-0.37359158
Median Absolute Deviation (MAD)0.10233378
Skewness1.6311461
Sum-2021.1304
Variance0.13144679
MonotonicityNot monotonic
2025-06-10T13:53:00.399311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5617617 3
 
0.1%
-0.50767875 3
 
0.1%
-0.5406518 2
 
< 0.1%
-0.53682566 2
 
< 0.1%
0.50562495 2
 
< 0.1%
-0.5329638 2
 
< 0.1%
-0.47945094 2
 
< 0.1%
-0.40415084 2
 
< 0.1%
-0.55073494 2
 
< 0.1%
-0.5959276 2
 
< 0.1%
Other values (5388) 5388
99.6%
ValueCountFrequency (%)
-1.1483903 1
< 0.1%
-1.1401569 1
< 0.1%
-1.1099311 1
< 0.1%
-1.0998387 1
< 0.1%
-1.0993729 1
< 0.1%
-1.0891747 1
< 0.1%
-1.0634226 1
< 0.1%
-1.0391371 1
< 0.1%
-1.0390397 1
< 0.1%
-1.0369247 1
< 0.1%
ValueCountFrequency (%)
0.9962663 1
< 0.1%
0.9546397 1
< 0.1%
0.9452925 1
< 0.1%
0.906611 1
< 0.1%
0.9030236 1
< 0.1%
0.8917418 1
< 0.1%
0.8745094 1
< 0.1%
0.86896515 1
< 0.1%
0.85832447 1
< 0.1%
0.85512525 1
< 0.1%

Fraud_Probability_Percent
Real number (ℝ)

High correlation 

Distinct5401
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.013172
Minimum0.022823537
Maximum99.438225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2025-06-10T13:53:00.637392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.022823537
5-th percentile0.098103074
Q10.31666447
median1.0029438
Q37.0688569
95-th percentile93.668436
Maximum99.438225
Range99.415401
Interquartile range (IQR)6.7521924

Descriptive statistics

Standard deviation29.431393
Coefficient of variation (CV)1.9603714
Kurtosis2.3045603
Mean15.013172
Median Absolute Deviation (MAD)0.86339175
Skewness1.9765938
Sum81221.26
Variance866.20688
MonotonicityNot monotonic
2025-06-10T13:53:00.888282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1222939 3
 
0.1%
0.09220703 3
 
0.1%
0.15959388 2
 
< 0.1%
0.0902706 2
 
< 0.1%
0.04993615 2
 
< 0.1%
0.22830269 2
 
< 0.1%
0.1508889 2
 
< 0.1%
4.3603277 1
 
< 0.1%
0.13410501 1
 
< 0.1%
0.12702413 1
 
< 0.1%
Other values (5391) 5391
99.6%
ValueCountFrequency (%)
0.022823537 1
< 0.1%
0.02561417 1
< 0.1%
0.026063185 1
< 0.1%
0.026425801 1
< 0.1%
0.027018946 1
< 0.1%
0.027636547 1
< 0.1%
0.028678546 1
< 0.1%
0.029498355 1
< 0.1%
0.03146881 1
< 0.1%
0.032562297 1
< 0.1%
ValueCountFrequency (%)
99.438225 1
< 0.1%
99.43239 1
< 0.1%
99.41806 1
< 0.1%
99.40916 1
< 0.1%
99.40706 1
< 0.1%
99.39267 1
< 0.1%
99.331375 1
< 0.1%
99.322235 1
< 0.1%
99.28554 1
< 0.1%
99.23529 1
< 0.1%

Top1_ActualValue
Real number (ℝ)

Distinct3077
Distinct (%)57.1%
Missing18
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean84306.799
Minimum0
Maximum5996050
Zeros16
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2025-06-10T13:53:01.125604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.0740455
Q1607.5
median7305
Q331645
95-th percentile465012
Maximum5996050
Range5996050
Interquartile range (IQR)31037.5

Descriptive statistics

Standard deviation271187.86
Coefficient of variation (CV)3.2166784
Kurtosis88.054632
Mean84306.799
Median Absolute Deviation (MAD)7293
Skewness7.3618808
Sum4.5458226 × 108
Variance7.3542854 × 1010
MonotonicityNot monotonic
2025-06-10T13:53:01.396506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 176
 
3.3%
10 43
 
0.8%
13 38
 
0.7%
35 36
 
0.7%
9 30
 
0.6%
6 28
 
0.5%
11 27
 
0.5%
12 26
 
0.5%
100 26
 
0.5%
15 23
 
0.4%
Other values (3067) 4939
91.3%
ValueCountFrequency (%)
0 16
0.3%
0.2767857143 1
 
< 0.1%
0.2857142857 1
 
< 0.1%
0.3225806452 1
 
< 0.1%
0.3666666667 1
 
< 0.1%
0.435483871 1
 
< 0.1%
0.4489795918 1
 
< 0.1%
0.45 1
 
< 0.1%
0.4556962025 1
 
< 0.1%
0.4615384615 2
 
< 0.1%
ValueCountFrequency (%)
5996050 1
< 0.1%
4713830 1
< 0.1%
3212000 1
< 0.1%
3133880 1
< 0.1%
2969530 1
< 0.1%
2914700 1
< 0.1%
2831940 1
< 0.1%
2744870 1
< 0.1%
2612740 1
< 0.1%
2540130 1
< 0.1%

Top2_ActualValue
Real number (ℝ)

Zeros 

Distinct1125
Distinct (%)20.9%
Missing35
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean5760.4553
Minimum0
Maximum292220
Zeros515
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2025-06-10T13:53:01.974331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q390
95-th percentile32452
Maximum292220
Range292220
Interquartile range (IQR)89

Descriptive statistics

Standard deviation27690.13
Coefficient of variation (CV)4.8069342
Kurtosis35.52537
Mean5760.4553
Median Absolute Deviation (MAD)5
Skewness5.7580468
Sum30962447
Variance7.6674327 × 108
MonotonicityNot monotonic
2025-06-10T13:53:02.277525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1257
23.2%
0 515
 
9.5%
35 188
 
3.5%
100 86
 
1.6%
10 68
 
1.3%
20 66
 
1.2%
5 57
 
1.1%
80 55
 
1.0%
4 52
 
1.0%
40 50
 
0.9%
Other values (1115) 2981
55.1%
ValueCountFrequency (%)
0 515
9.5%
0.03125 1
 
< 0.1%
0.03703703704 1
 
< 0.1%
0.05263157895 1
 
< 0.1%
0.07484407484 1
 
< 0.1%
0.08232445521 1
 
< 0.1%
0.08333333333 1
 
< 0.1%
0.08474576271 1
 
< 0.1%
0.1 1
 
< 0.1%
0.1052631579 1
 
< 0.1%
ValueCountFrequency (%)
292220 1
< 0.1%
268340 1
< 0.1%
259850 1
< 0.1%
255250 1
< 0.1%
240840 1
< 0.1%
237930 1
< 0.1%
237000 1
< 0.1%
236720 1
< 0.1%
226800 1
< 0.1%
224960 1
< 0.1%

Top3_ActualValue
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct2295
Distinct (%)45.1%
Missing317
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean5489.0327
Minimum-102073.04
Maximum638766.96
Zeros693
Zeros (%)12.8%
Negative10
Negative (%)0.2%
Memory size42.4 KiB
2025-06-10T13:53:02.531249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-102073.04
5-th percentile0
Q10.41666667
median1.1296296
Q370
95-th percentile34068
Maximum638766.96
Range740840
Interquartile range (IQR)69.583333

Descriptive statistics

Standard deviation26612.993
Coefficient of variation (CV)4.848394
Kurtosis92.627861
Mean5489.0327
Median Absolute Deviation (MAD)1.1296296
Skewness7.2963463
Sum27955643
Variance7.0825141 × 108
MonotonicityNot monotonic
2025-06-10T13:53:02.756980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 693
 
12.8%
1 310
 
5.7%
20 120
 
2.2%
0.5 95
 
1.8%
0.3333333333 51
 
0.9%
10 47
 
0.9%
100 46
 
0.9%
35 46
 
0.9%
9 38
 
0.7%
8 35
 
0.6%
Other values (2285) 3612
66.8%
(Missing) 317
 
5.9%
ValueCountFrequency (%)
-102073.0388 1
< 0.1%
-102063.0388 1
< 0.1%
-100123.0388 1
< 0.1%
-99183.03882 1
< 0.1%
-96713.03882 1
< 0.1%
-94803.03882 1
< 0.1%
-89573.03882 1
< 0.1%
-84943.03882 1
< 0.1%
-80153.03882 1
< 0.1%
-77943.03882 1
< 0.1%
ValueCountFrequency (%)
638766.9612 1
< 0.1%
305026.9612 1
< 0.1%
298506.9612 1
< 0.1%
271910 1
< 0.1%
239050 1
< 0.1%
235000 1
< 0.1%
229780 1
< 0.1%
218640 1
< 0.1%
213380 1
< 0.1%
210190 1
< 0.1%

Interactions

2025-06-10T13:52:53.953058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:43.927529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:45.443557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:47.176336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:48.556579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:49.954654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:51.381015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:52.578747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:54.134801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:44.122988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:45.623998image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:47.330267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:48.746096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:50.127900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:51.540860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:52.723374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:54.304076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:44.287652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:45.789813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:47.471000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:48.911373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:50.313295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:51.677645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:52.862398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:54.466947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:44.447979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:45.972825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:47.612793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:49.077320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:50.481105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:51.821495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:53.017423image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:54.643953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:44.615651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:46.140758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:47.783162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:49.219319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:50.658056image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:51.956262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:53.173487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:54.853898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:44.799475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:46.325349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:47.951234image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:49.396761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:50.856217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:52.105124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:53.367978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:55.051848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:45.004529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:46.856223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:48.151944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:49.573381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:51.051170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:52.253880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:53.580513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:55.239157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:45.211628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:47.009205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:48.339156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:49.763271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:51.210951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:52.420445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-10T13:52:53.761916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-06-10T13:53:02.932192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Fraud_ProbabilityFraud_Probability_PercentTop1_ActualValueTop1_FeatureTop1_SHAPTop2_ActualValueTop2_FeatureTop2_SHAPTop3_ActualValueTop3_FeatureTop3_SHAP
Fraud_Probability1.0001.0000.3510.1400.8010.4660.2310.3940.4030.2280.469
Fraud_Probability_Percent1.0001.0000.3510.1400.8010.4660.2310.3940.4030.2280.469
Top1_ActualValue0.3510.3511.0000.0000.3510.2200.0910.2000.1360.1870.204
Top1_Feature0.1400.1400.0001.0000.3000.2010.3090.1400.2020.1930.085
Top1_SHAP0.8010.8010.3510.3001.0000.4040.2740.2400.4100.2950.224
Top2_ActualValue0.4660.4660.2200.2010.4041.0000.3340.1820.1740.0780.241
Top2_Feature0.2310.2310.0910.3090.2740.3341.0000.3450.0670.1920.204
Top2_SHAP0.3940.3940.2000.1400.2400.1820.3451.0000.1150.2390.402
Top3_ActualValue0.4030.4030.1360.2020.4100.1740.0670.1151.0000.5630.162
Top3_Feature0.2280.2280.1870.1930.2950.0780.1920.2390.5631.0000.357
Top3_SHAP0.4690.4690.2040.0850.2240.2410.2040.4020.1620.3571.000

Missing values

2025-06-10T13:52:55.835664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-10T13:52:56.174926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-10T13:52:56.439541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ProviderFraud_ProbabilityTop1_FeatureTop1_SHAPTop2_FeatureTop2_SHAPTop3_FeatureTop3_SHAPFraud_Probability_PercentTop1_ActualValueTop2_ActualValueTop3_ActualValue
0PRV521450.018644Provider_Insurance_Claim_Reimbursement_Amt-1.030070prv_avg_claims_indicator-0.586857DeductibleAmtPaid_sum-0.4986921.86435760910.01.038136650.000000
1PRV551040.016717Provider_Insurance_Claim_Reimbursement_Amt-1.947104DeductibleAmtPaid_sum-0.676061HospitalDuration_max-0.2631121.67167314880.0380.000000NaN
2PRV548940.982605Provider_Insurance_Claim_Reimbursement_Amt2.280505HospitalDuration_max0.655633OperatingPhysician_nunique0.23421198.2604701757060.035.00000031.000000
3PRV549270.003985Provider_Insurance_Claim_Reimbursement_Amt-1.784703DeductibleAmtPaid_sum-0.727324prv_avg_claims_indicator-0.4362680.39851822600.050.0000001.085106
4PRV552150.983695Provider_Insurance_Claim_Reimbursement_Amt2.404095HospitalDuration_max0.535668HospitalDuration_sum-0.25631998.3694702284560.034.000000792.000000
5PRV549500.794330HospitalDuration_max1.093456prv_avg_claims_indicator-0.617702HospitalDuration_std0.60556579.43298027.01.00000010.059821
6PRV573170.981709Provider_Insurance_Claim_Reimbursement_Amt2.511147HospitalDuration_max0.570222ClmAdmitDiagnosisCode_Count0.27503498.170900926330.022.000000164.000000
7PRV573330.146077Provider_Insurance_Claim_Reimbursement_Amt0.836762HospitalDuration_max-0.409759ChronicCond_Heartfailure_mean-0.38647114.607726291020.0NaN0.527888
8PRV531000.061861Provider_Insurance_Claim_Reimbursement_Amt-0.825458HospitalDuration_max-0.697472HospitalDuration_std-0.4516506.18609786880.09.0000003.847077
9PRV560210.014879Provider_Insurance_Claim_Reimbursement_Amt-1.280189DeductibleAmtPaid_sum-0.691491prv_avg_claims_indicator-0.4709461.48786539920.0150.0000001.032258
ProviderFraud_ProbabilityTop1_FeatureTop1_SHAPTop2_FeatureTop2_SHAPTop3_FeatureTop3_SHAPFraud_Probability_PercentTop1_ActualValueTop2_ActualValueTop3_ActualValue
5400PRV528040.001116Provider_Insurance_Claim_Reimbursement_Amt-1.135756prv_avg_claims_indicator-0.707482HospitalDuration_max-0.5314980.11156636000.01.013.0
5401PRV556370.011534Provider_Insurance_Claim_Reimbursement_Amt-1.995208prv_avg_claims_indicator-0.746210ChronicCond_KidneyDisease_mean0.6194161.153426300.01.01.0
5402PRV577580.002682Provider_Insurance_Claim_Reimbursement_Amt-2.015305prv_avg_claims_indicator-0.684711DeductibleAmtPaid_sum-0.4651720.268191110.01.00.0
5403PRV576550.000462Provider_Insurance_Claim_Reimbursement_Amt-2.217601prv_avg_claims_indicator-0.706257ChronicCond_KidneyDisease_mean-0.5957240.046188400.01.00.0
5404PRV542950.001375Provider_Insurance_Claim_Reimbursement_Amt-2.116444prv_avg_claims_indicator-0.791678ChronicCond_KidneyDisease_mean-0.5494220.1374603300.01.00.0
5405PRV560560.000903Provider_Insurance_Claim_Reimbursement_Amt-2.128856prv_avg_claims_indicator-0.718056ChronicCond_Heartfailure_mean-0.5959280.09027140.01.00.0
5406PRV548200.008429Provider_Insurance_Claim_Reimbursement_Amt-1.418610prv_avg_claims_indicator-0.967576HospitalDuration_max-0.6025000.84292812000.01.02.0
5407PRV560290.000554Provider_Insurance_Claim_Reimbursement_Amt-2.156970prv_avg_claims_indicator-0.591558ChronicCond_KidneyDisease_mean-0.5617620.055379100.01.00.0
5408PRV517510.001722Provider_Insurance_Claim_Reimbursement_Amt-2.254718prv_avg_claims_indicator-0.734502DeductibleAmtPaid_sum-0.4491370.172225900.01.00.0
5409PRV554050.011111Provider_Insurance_Claim_Reimbursement_Amt-2.017444ChronicCond_Heartfailure_mean-0.737984DeductibleAmtPaid_sum-0.5375281.1111412730.00.0200.0